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Forecasting implied volatility in foreign exchange markets: a functional time series approach

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  • Fearghal Kearney
  • Mark Cummins
  • Finbarr Murphy

Abstract

We utilise novel functional time series (FTS) techniques to characterise and forecast implied volatility in foreign exchange markets. In particular, we examine the daily implied volatility curves of FX options, namely; Euro/United States Dollar, Euro/British Pound, and Euro/Japanese Yen. The FTS model is shown to produce both realistic and plausible implied volatility shapes that closely match empirical data during the volatile 2006–2013 period. Furthermore, the FTS model significantly outperforms implied volatility forecasts produced by traditionally employed parametric models. The evaluation is performed under both in-sample and out-of-sample testing frameworks with our findings shown to be robust across various currencies, moneyness segments, contract maturities, forecasting horizons, and out-of-sample window lengths. The economic significance of the results is highlighted through the implementation of a simple trading strategy.

Suggested Citation

  • Fearghal Kearney & Mark Cummins & Finbarr Murphy, 2018. "Forecasting implied volatility in foreign exchange markets: a functional time series approach," The European Journal of Finance, Taylor & Francis Journals, vol. 24(1), pages 1-18, January.
    • Handle: RePEc:taf:eurjfi:v:24:y:2018:i:1:p:1-18
    DOI: 10.1080/1351847X.2016.1271441
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    Cited by:

    1. Shang, Han Lin & Kearney, Fearghal, 2022. "Dynamic functional time-series forecasts of foreign exchange implied volatility surfaces," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1025-1049.
    2. Han Lin Shang & Yang Yang & Fearghal Kearney, 2019. "Intraday forecasts of a volatility index: functional time series methods with dynamic updating," Annals of Operations Research, Springer, vol. 282(1), pages 331-354, November.
    3. Marek Vochozka & Jakub Horák & Petr Šuleř, 2019. "Equalizing Seasonal Time Series Using Artificial Neural Networks in Predicting the Euro–Yuan Exchange Rate," JRFM, MDPI, vol. 12(2), pages 1-17, April.
    4. Francisco Martínez-Álvarez & Amandine Schmutz & Gualberto Asencio-Cortés & Julien Jacques, 2018. "A Novel Hybrid Algorithm to Forecast Functional Time Series Based on Pattern Sequence Similarity with Application to Electricity Demand," Energies, MDPI, vol. 12(1), pages 1-18, December.

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